Scalable Software Architecture for Dynamic Threat Detection and Mitigation In IOT

In an era of heightened interconnectivity, safeguarding Internet of Things (IoT) environments against intrusions is critical. This paper introduces an innovative Machine Learning Framework for Intrusion Detection in IoT Environments, integrating a Generative Adversarial Network (GAN) module and employing the SMOTE (Synthetic Minority Over-sampling Technique) technique. Utilizing meticulously curated datasets, our approach encompasses data preprocessing and feature engineering, enhancing data quality and relevance. The framework harnesses a suite of machine learning algorithms, now augmented with GAN for improved data generation and SMOTE for addressing class imbalance, resulting in more accurate intrusion detection. Experimental evaluation reveals the framework's superiority over baseline methods, showcasing significantly heightened accuracy, precision, and recall. This research constitutes a substantial stride in bolstering IoT security, providing a robust and adaptable solution to fortify IoT ecosystems against diverse intrusion threats.

Keywords: IoT security, intrusion detection, machine learning, data preprocessing, feature engineering.